During my time as a product manager, Chief Data Officer and Data Strategist, I’ve come across many quick fix solutions to tackle the business glossary challenge including, “we have an Excel-based solution.” In reviewing the solution with the client, more often than not, it does effectively inventory and define terms, but it soon becomes apparent that this is an overly simplistic view of data beyond a simple cataloging of terms with limited ability to capture the important relationships and gain valuable insights into this important enterprise asset.
Taking a business user’s perspective when looking for the right information to consume, the search for data typically starts by looking for the likely candidates by performing some form of key word searches – easy enough. Next, they will want to answer questions related to the quality level of the data, find out how to receive the data and gain an understanding of how and where they may be used. They may want to initiate a conversation with the data owner or leverage a mind share of knowledge through collaboration with other peers. Data consumers have the right to be able to answer these questions quickly, but these types of inquiries will not be sufficiently addressed with the simplified and flattened view of data being provided by Excel or SharePoint-based solutions.
“Data alone does not prove value, it is its movement and interrelationships with people, processes, technology and other data assets that generates value.”
Knowing these relationships, ensuring the data is properly managed and kept current are many of the capabilities of an enterprise business glossary. Users should be able to get answers to the following questions about data and its inter dependencies:
In order to gain comprehensive insights into the deep understanding of data, we need visibility into a number of dimensions of the data such as consumption, technical and business representation, associated policies and rules, as well as the roles and responsibilities associated with the various data assets. An enterprise solution provides users with these capabilities and with the ability to appropriately structure and synthesize the dimensions that surround your data.
There are many cases in an organization when new projects rely on data where it is shared across departments and even business units. When business lines are dependent on each other’s data, a robust business glossary becomes critical as each business unit has its own priorities, dialect and functional use of information that may be the same or differ in definition and rights from other consumers. It therefore becomes an imperative to leverage a robust solution that shares knowledge to meet everyone’s objectives. Think of the business glossary as being the card catalog in a library. Books can cross many different classifications such as thriller, autobiography, history or geography. They can have specific attributes that can help you search for the book such as genre, publisher, format, author and publication date. In much the same way, a business glossary can use various classifications and content about data to aid in the search, availability and usage of the enterprise data. Having this searchable catalog at an enterprise level provides a level of transparency around helping to avoid ambiguity around the data in use.
A business glossary defines not only the data vocabulary across an entire enterprise, but ensures consistency of business terms. It synthesizes all the details about an enterprise’s data assets across a multitude of data dictionaries and organizes it into a simple, easy to understand format. Glossaries bridge the business and technical divide by providing transparency into definitions, synonyms and important business attributes while tying these important attributes to the more technical definitions stored within the various critical system, reports or processes. It also identifies the owners of data and subject matter experts while enabling collaboration between different departments.
Let’s consider a simple example where the marketing department has a field in their database that simply says “Name.” That could be a first name, a full name, or a last name. It’s not clear. A business glossary identifies this discrepancy and creates a field that is more clearly labeled with the right business context information. It then becomes clear to all users that Name means full name, with first name first and then last name.
A glossary can also provide lineage so the enterprise can understand the flow and dependencies of the data. It can also identify critical business process relationships, provide transparency into the various data quality dimensions and communicate data access methods and usage restrictions to data consumers. Having common data definitions and transparency, users can easily communicate and ensure they are using the right data for the right purpose.
Delivering a comprehensive understanding of an enterprises’ data, the business glossary can enable data owners, data stewards, and data consumers to effectively manage and apply data to extract maximum business value within their functional areas while being cognizant of the other consumers of the shared data assets.
Business glossaries don’t just exist; they must be created over a period of months or even years. Depending on the size of the organization, business glossaries can be complicated because data is complicated. Different people in different departments have different perspectives on data. Getting cross departmental agreement on standard definitions based on individual perspectives of data is a strenuous task. Unless, of course it’s automated.
In order to create a comprehensive business glossary, enterprises should implement a data governance solution that connects the dots of data lineage, data definitions, data quality, with the business glossaries. The solution should provide the ability to not only measure the outcome of data quality rules, but also articulate the impact of the data quality by the expectations of the business. The suite should offer data consumers transparency into the ownership dimension, and ensure fluid communications between the data owners and the data consumers. The platform should have extensive collaboration capabilities in order for users to gain expertise on their data.
Finally, the right solution should have automatic discovery capabilities, enabling the capture and monitoring of changes to metadata. Once changes are discovered, the technical metadata relationships may be investigated to deliver meaningful insights on data. With the proper data governance suite, enterprises can successfully create a comprehensive business glossary that is user friendly, flexible and most importantly maintainable.
To learn more about data governance and business glossaries, download the data sheet below.
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